Comprehensive Radiomics Analysis to Enhance Breast Lesion Characterization Using QUS Spectral Parametric Imaging.

Background/

Objectives

We aimed to evaluate the generalizability of QUS spectral parametric imaging radiomics for breast tumor characterization in a large cohort through methodological optimization and rigorous validation, including comprehensive feature extraction and evaluation of multiple classifiers, addressing limitations of previous studies potentially affected by data leakage.

Methods

This study included 277 participants (185 malignant cases (median age, 51 years [IQR: 44-63 years]) and 92 benign cases (median age, 46 years [IQR: 38-51 years]) with breast masses, acquired between September 2014 and October 2021. QUS spectroscopic analysis resulted in five maps, from which first-order statistical, various textural, and morphological features were extracted from both the tumor core and a surrounding 5 mm margin. The ground truth label was determined from histopathological analysis. Predictive models were developed to distinguish malignant from benign lesions. Their generalization performance was assessed using hold-out validation. Beyond assessing models' performance, SHapley Additive exPlanations (SHAP) analysis identified the most influential features to the predictions.

Results

329 radiomics features demonstrated statistically significant differences (p-values < 0.00005). Averaged across 50 partitions, SVM-Linear models produced test performance of 82 ± 8% (CI: 59-97) recall, 79 ± 9% (CI: 50-100) specificity, and 0.87 ± 0.05 (CI: 0.71-0.98) area under the receiver operating characteristic curve (AUROC). The best single partition performance with an SVM-Linear model was 92% recall, 89% specificity, and 0.97 AUROC.

Conclusions

This work establishes a performance benchmark for handcrafted radiomic features derived from QUS spectral parametric images in breast lesion characterization, demonstrating its strong generalization and servicing as a reference for future model development.